Artificial Intelligence (AI) at the network edge is changing the way we interact with technology. With the explosion of the Internet of Things (IoT), there is now an unprecedented number of devices that require connectivity. To cater to this demand, network architectures have evolved to include edge computing, which places computing and storage resources closer to IoT devices. By doing so, there is less latency, faster data analysis, and reduced network congestion.
The use of AI at the network edge further enhances this technology by providing advanced analytics that can predict device failures, optimize network traffic, improve device security, and even enhance customer experiences. With AI at the network edge, there is no need to send all data to a central location for processing, resulting in faster response times and more efficient use of computing resources. This article will explore how to get AI at the network edge, its benefits, and its real-life examples.
How does AI work at the network edge?
There are two ways to deploy AI at the network edge. The first method is to use the traditional, cloud-based AI architecture where data is sent to a central location for processing. The results are then sent back to the edge devices for further processing. While this method provides accurate results, there is a delay due to the round-trip time required for sending data to the cloud for processing and receiving the results.
The second method is to use AI models that are specifically designed to run at the network edge. These AI models are optimized for real-time processing, low power consumption, and small form factors. By using this method, devices at the edge can process data without sending it to a central location, eliminating latency issues and reducing network congestion.
What are the advantages of AI at the network edge?
Using AI at the network edge brings many advantages. Firstly, it enables real-time decision-making, which is critical for services like autonomous driving and real-time monitoring. For instance, in the case of an autonomous car driving in a city, the car must make decisions in real-time to avoid collisions, pedestrians and other vehicles on the road. The ability to process data in real-time at the edge allows such devices to operate safely and efficiently.
Secondly, AI at the network edge allows for predictive maintenance, which is critical in highly distributed environments. By analyzing data from devices at the edge, AI algorithms can identify patterns and detect early failures, enabling maintenance teams to act proactively. This saves costs by reducing the number of unscheduled downtime events while increasing productivity.
Thirdly, AI at the network edge allows for more efficient use of computing resources. By processing data locally, devices use less bandwidth, reducing network congestion and accelerating data analysis. This enables faster response times, which is critical for time-sensitive services like healthcare.
Real-life Examples of AI at the network edge
Deployment of AI at the network edge is not confined to the tech sector only. Major industries like healthcare, manufacturing, and transportation also have applications for AI in their workflows.
One such example is the use of AI at the edge in Healthcare. The rapid growth of IoT devices in the healthcare industry has led to the creation of large volumes of health data. It is now possible to collect data from wearable devices, monitoring equipment, and various sensors. Edge computing, when combined with AI, helps create a personalized care experience that functions 24/7. Wearable devices can pick up patient data right from the beginning of their hospital stay to diagnose or predict issues. The same data is also analyzed to develop special communication threads that can track the patient’s progress, provide answers on-demand and handle emergency situations, even when the care team isn’t physically present.
Another example is in logistics where AI at the network edge can optimize the delivery routes of courier companies. By analyzing data from GPS trackers, weather reports, and road conditions, AI can optimize fuel consumption and reduce travel time. For instance, DHL deployed a parcelcopter, an autonomous drone capable of transporting goods long distances through challenging terrain, such as high mountains or rough seas.
The future of AI at the network edge
As we continue to build smarter and more connected devices, the need for AI at the network edge will increase. With 5G on the horizon, the demand for real-time decision-making and distributed computing will become even more crucial. With AI at the network edge, devices can operate efficiently and safely without relying on central cloud infrastructure.
Also, as concerns about data privacy and security continue to rise, AI at the network edge can help mitigate those concerns. By keeping sensitive data close to the device instead of sending it to a remote server, there is less risk of data breaches and other security issues.
In conclusion, AI at the network edge is a game-changer for the IoT industry. With its many benefits, including real-time decision-making, predictive maintenance, and efficient resource utilization, it is unsurprising that many industries are already adopting this technology. It is only a matter of time before this becomes the norm in everyday applications.